Overview

Dataset statistics

Number of variables28
Number of observations99991
Missing cells102585
Missing cells (%)3.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory97.3 MiB
Average record size in memory1019.9 B

Variable types

Text7
Categorical6
Numeric15

Alerts

Annual_Income is highly overall correlated with Monthly_Inhand_Salary and 2 other fieldsHigh correlation
Monthly_Inhand_Salary is highly overall correlated with Annual_Income and 2 other fieldsHigh correlation
Num_Bank_Accounts is highly overall correlated with Interest_Rate and 2 other fieldsHigh correlation
Interest_Rate is highly overall correlated with Num_Bank_Accounts and 4 other fieldsHigh correlation
NumofLoan is highly overall correlated with OutstandingDebtHigh correlation
Delay_from_due_date is highly overall correlated with Num_Bank_Accounts and 5 other fieldsHigh correlation
NumofDelayedPayment is highly overall correlated with Num_Bank_Accounts and 2 other fieldsHigh correlation
NumCreditInquiries is highly overall correlated with Interest_Rate and 2 other fieldsHigh correlation
OutstandingDebt is highly overall correlated with Interest_Rate and 5 other fieldsHigh correlation
Amountinvestedmonthly is highly overall correlated with Annual_Income and 1 other fieldsHigh correlation
MonthlyBalance is highly overall correlated with Annual_Income and 1 other fieldsHigh correlation
CreditMix is highly overall correlated with Delay_from_due_date and 2 other fieldsHigh correlation
Payment_of_Min_Amount is highly overall correlated with CreditMixHigh correlation
Credit_Score is highly overall correlated with OutstandingDebtHigh correlation
Name has 9985 (10.0%) missing valuesMissing
SSN has 5572 (5.6%) missing valuesMissing
Occupation has 7061 (7.1%) missing valuesMissing
Monthly_Inhand_Salary has 15001 (15.0%) missing valuesMissing
Type_of_Loan has 11408 (11.4%) missing valuesMissing
NumofDelayedPayment has 7002 (7.0%) missing valuesMissing
ChangedCreditLimit has 2091 (2.1%) missing valuesMissing
NumCreditInquiries has 1965 (2.0%) missing valuesMissing
CreditMix has 20193 (20.2%) missing valuesMissing
Credit_History_Age has 9029 (9.0%) missing valuesMissing
Amountinvestedmonthly has 4478 (4.5%) missing valuesMissing
Payment_Behaviour has 7600 (7.6%) missing valuesMissing
MonthlyBalance has 1200 (1.2%) missing valuesMissing
Month is uniformly distributedUniform
ID has unique valuesUnique
Num_Bank_Accounts has 4328 (4.3%) zerosZeros
NumofLoan has 10930 (10.9%) zerosZeros
Delay_from_due_date has 1195 (1.2%) zerosZeros
NumofDelayedPayment has 1608 (1.6%) zerosZeros
NumCreditInquiries has 6972 (7.0%) zerosZeros
TotalEMIpermonth has 10613 (10.6%) zerosZeros

Reproduction

Analysis started2023-11-20 14:30:38.155975
Analysis finished2023-11-20 14:31:09.590227
Duration31.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

ID
Text

UNIQUE 

Distinct99991
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.8 MiB
2023-11-20T14:31:09.953025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.6006541
Min length6

Characters and Unicode

Total characters660006
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique99991 ?
Unique (%)100.0%

Sample

1st row0x1602
2nd row0x1603
3rd row0x1604
4th row0x1605
5th row0x1606
ValueCountFrequency (%)
0x1602 1
 
< 0.1%
0x164c 1
 
< 0.1%
0x1606 1
 
< 0.1%
0x1607 1
 
< 0.1%
0x1608 1
 
< 0.1%
0x1609 1
 
< 0.1%
0x160e 1
 
< 0.1%
0x160f 1
 
< 0.1%
0x1610 1
 
< 0.1%
0x1611 1
 
< 0.1%
Other values (99981) 99981
> 99.9%
2023-11-20T14:31:10.485146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 124096
18.8%
x 99991
15.2%
1 69499
10.5%
2 43210
 
6.5%
4 26837
 
4.1%
3 26834
 
4.1%
5 26825
 
4.1%
a 24279
 
3.7%
7 24278
 
3.7%
b 24275
 
3.7%
Other values (7) 169882
25.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 414395
62.8%
Lowercase Letter 245611
37.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 124096
29.9%
1 69499
16.8%
2 43210
 
10.4%
4 26837
 
6.5%
3 26834
 
6.5%
5 26825
 
6.5%
7 24278
 
5.9%
8 24273
 
5.9%
9 24273
 
5.9%
6 24270
 
5.9%
Lowercase Letter
ValueCountFrequency (%)
x 99991
40.7%
a 24279
 
9.9%
b 24275
 
9.9%
d 24275
 
9.9%
e 24273
 
9.9%
c 24270
 
9.9%
f 24248
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
Common 414395
62.8%
Latin 245611
37.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 124096
29.9%
1 69499
16.8%
2 43210
 
10.4%
4 26837
 
6.5%
3 26834
 
6.5%
5 26825
 
6.5%
7 24278
 
5.9%
8 24273
 
5.9%
9 24273
 
5.9%
6 24270
 
5.9%
Latin
ValueCountFrequency (%)
x 99991
40.7%
a 24279
 
9.9%
b 24275
 
9.9%
d 24275
 
9.9%
e 24273
 
9.9%
c 24270
 
9.9%
f 24248
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 660006
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 124096
18.8%
x 99991
15.2%
1 69499
10.5%
2 43210
 
6.5%
4 26837
 
4.1%
3 26834
 
4.1%
5 26825
 
4.1%
a 24279
 
3.7%
7 24278
 
3.7%
b 24275
 
3.7%
Other values (7) 169882
25.7%
Distinct12500
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size7.1 MiB
2023-11-20T14:31:10.791536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9395146
Min length9

Characters and Unicode

Total characters993862
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCUS_0xd40
2nd rowCUS_0xd40
3rd rowCUS_0xd40
4th rowCUS_0xd40
5th rowCUS_0xd40
ValueCountFrequency (%)
cus_0xd40 8
 
< 0.1%
cus_0x4100 8
 
< 0.1%
cus_0x609d 8
 
< 0.1%
cus_0x4d43 8
 
< 0.1%
cus_0xa5f9 8
 
< 0.1%
cus_0x5b48 8
 
< 0.1%
cus_0xc0ab 8
 
< 0.1%
cus_0x2dbc 8
 
< 0.1%
cus_0xb891 8
 
< 0.1%
cus_0x1cdb 8
 
< 0.1%
Other values (12490) 99911
99.9%
2023-11-20T14:31:11.218641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 118237
11.9%
C 99991
 
10.1%
S 99991
 
10.1%
_ 99991
 
10.1%
x 99991
 
10.1%
U 99991
 
10.1%
4 27999
 
2.8%
6 27399
 
2.8%
5 27196
 
2.7%
3 27071
 
2.7%
Other values (11) 266005
26.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 361174
36.3%
Uppercase Letter 299973
30.2%
Lowercase Letter 232724
23.4%
Connector Punctuation 99991
 
10.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 118237
32.7%
4 27999
 
7.8%
6 27399
 
7.6%
5 27196
 
7.5%
3 27071
 
7.5%
8 27067
 
7.5%
7 26772
 
7.4%
9 26733
 
7.4%
2 26717
 
7.4%
1 25983
 
7.2%
Lowercase Letter
ValueCountFrequency (%)
x 99991
43.0%
b 26798
 
11.5%
a 26543
 
11.4%
c 22271
 
9.6%
e 19485
 
8.4%
d 18871
 
8.1%
f 18765
 
8.1%
Uppercase Letter
ValueCountFrequency (%)
C 99991
33.3%
S 99991
33.3%
U 99991
33.3%
Connector Punctuation
ValueCountFrequency (%)
_ 99991
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 532697
53.6%
Common 461165
46.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 118237
25.6%
_ 99991
21.7%
4 27999
 
6.1%
6 27399
 
5.9%
5 27196
 
5.9%
3 27071
 
5.9%
8 27067
 
5.9%
7 26772
 
5.8%
9 26733
 
5.8%
2 26717
 
5.8%
Latin
ValueCountFrequency (%)
C 99991
18.8%
S 99991
18.8%
x 99991
18.8%
U 99991
18.8%
b 26798
 
5.0%
a 26543
 
5.0%
c 22271
 
4.2%
e 19485
 
3.7%
d 18871
 
3.5%
f 18765
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 993862
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 118237
11.9%
C 99991
 
10.1%
S 99991
 
10.1%
_ 99991
 
10.1%
x 99991
 
10.1%
U 99991
 
10.1%
4 27999
 
2.8%
6 27399
 
2.8%
5 27196
 
2.7%
3 27071
 
2.7%
Other values (11) 266005
26.8%

Month
Categorical

UNIFORM 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.7 MiB
January
12500 
May
12500 
June
12500 
March
12499 
April
12499 
Other values (3)
37493 

Length

Max length8
Median length6
Mean length5.2499525
Min length3

Characters and Unicode

Total characters524948
Distinct characters19
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJanuary
2nd rowFebruary
3rd rowMarch
4th rowApril
5th rowMay

Common Values

ValueCountFrequency (%)
January 12500
12.5%
May 12500
12.5%
June 12500
12.5%
March 12499
12.5%
April 12499
12.5%
August 12499
12.5%
February 12497
12.5%
July 12497
12.5%

Length

2023-11-20T14:31:11.351956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T14:31:11.468518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
january 12500
12.5%
may 12500
12.5%
june 12500
12.5%
march 12499
12.5%
april 12499
12.5%
august 12499
12.5%
february 12497
12.5%
july 12497
12.5%

Most occurring characters

ValueCountFrequency (%)
u 74992
14.3%
a 62496
11.9%
r 62492
11.9%
y 49994
9.5%
J 37497
 
7.1%
n 25000
 
4.8%
M 24999
 
4.8%
A 24998
 
4.8%
e 24997
 
4.8%
l 24996
 
4.8%
Other values (9) 112487
21.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 424957
81.0%
Uppercase Letter 99991
 
19.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 74992
17.6%
a 62496
14.7%
r 62492
14.7%
y 49994
11.8%
n 25000
 
5.9%
e 24997
 
5.9%
l 24996
 
5.9%
t 12499
 
2.9%
s 12499
 
2.9%
g 12499
 
2.9%
Other values (5) 62493
14.7%
Uppercase Letter
ValueCountFrequency (%)
J 37497
37.5%
M 24999
25.0%
A 24998
25.0%
F 12497
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 524948
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 74992
14.3%
a 62496
11.9%
r 62492
11.9%
y 49994
9.5%
J 37497
 
7.1%
n 25000
 
4.8%
M 24999
 
4.8%
A 24998
 
4.8%
e 24997
 
4.8%
l 24996
 
4.8%
Other values (9) 112487
21.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 524948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 74992
14.3%
a 62496
11.9%
r 62492
11.9%
y 49994
9.5%
J 37497
 
7.1%
n 25000
 
4.8%
M 24999
 
4.8%
A 24998
 
4.8%
e 24997
 
4.8%
l 24996
 
4.8%
Other values (9) 112487
21.4%

Name
Text

MISSING 

Distinct10139
Distinct (%)11.3%
Missing9985
Missing (%)10.0%
Memory size6.8 MiB
2023-11-20T14:31:11.820746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length20
Mean length9.7698265
Min length2

Characters and Unicode

Total characters879343
Distinct characters57
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAaron Maashoh
2nd rowAaron Maashoh
3rd rowAaron Maashoh
4th rowAaron Maashoh
5th rowAaron Maashoh
ValueCountFrequency (%)
david 640
 
0.5%
jonathan 613
 
0.5%
jessica 505
 
0.4%
sarah 405
 
0.3%
karen 378
 
0.3%
nick 377
 
0.3%
caroline 372
 
0.3%
tim 371
 
0.3%
john 342
 
0.3%
tom 334
 
0.3%
Other values (9720) 121328
96.5%
2023-11-20T14:31:12.234372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 91804
 
10.4%
e 76032
 
8.6%
n 59299
 
6.7%
i 58581
 
6.7%
r 54508
 
6.2%
o 44666
 
5.1%
l 42110
 
4.8%
35694
 
4.1%
t 35055
 
4.0%
h 30588
 
3.5%
Other values (47) 351006
39.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 714687
81.3%
Uppercase Letter 125358
 
14.3%
Space Separator 35694
 
4.1%
Other Punctuation 2147
 
0.2%
Dash Punctuation 1457
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 91804
12.8%
e 76032
 
10.6%
n 59299
 
8.3%
i 58581
 
8.2%
r 54508
 
7.6%
o 44666
 
6.2%
l 42110
 
5.9%
t 35055
 
4.9%
h 30588
 
4.3%
s 30399
 
4.3%
Other values (16) 191645
26.8%
Uppercase Letter
ValueCountFrequency (%)
S 14298
 
11.4%
A 8732
 
7.0%
M 8671
 
6.9%
L 8377
 
6.7%
J 7972
 
6.4%
C 7787
 
6.2%
R 7132
 
5.7%
D 6998
 
5.6%
K 6893
 
5.5%
B 6531
 
5.2%
Other values (16) 41967
33.5%
Other Punctuation
ValueCountFrequency (%)
. 1098
51.1%
" 966
45.0%
, 83
 
3.9%
Space Separator
ValueCountFrequency (%)
35694
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1457
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 840045
95.5%
Common 39298
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 91804
 
10.9%
e 76032
 
9.1%
n 59299
 
7.1%
i 58581
 
7.0%
r 54508
 
6.5%
o 44666
 
5.3%
l 42110
 
5.0%
t 35055
 
4.2%
h 30588
 
3.6%
s 30399
 
3.6%
Other values (42) 317003
37.7%
Common
ValueCountFrequency (%)
35694
90.8%
- 1457
 
3.7%
. 1098
 
2.8%
" 966
 
2.5%
, 83
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 879343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 91804
 
10.4%
e 76032
 
8.6%
n 59299
 
6.7%
i 58581
 
6.7%
r 54508
 
6.2%
o 44666
 
5.1%
l 42110
 
4.8%
35694
 
4.1%
t 35055
 
4.0%
h 30588
 
3.5%
Other values (47) 351006
39.9%

Age
Text

Distinct1788
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size6.4 MiB
2023-11-20T14:31:12.617425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.1028993
Min length2

Characters and Unicode

Total characters210271
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1523 ?
Unique (%)1.5%

Sample

1st row23
2nd row23
3rd row-500
4th row23
5th row23
ValueCountFrequency (%)
38 2994
 
3.0%
28 2968
 
3.0%
31 2954
 
3.0%
26 2945
 
2.9%
32 2884
 
2.9%
36 2868
 
2.9%
35 2866
 
2.9%
25 2861
 
2.9%
27 2859
 
2.9%
39 2846
 
2.8%
Other values (1718) 70946
71.0%
2023-11-20T14:31:13.133678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 38381
18.3%
3 38347
18.2%
4 33427
15.9%
1 21430
10.2%
5 21312
10.1%
0 11669
 
5.5%
8 10410
 
5.0%
9 10318
 
4.9%
6 10069
 
4.8%
7 9084
 
4.3%
Other values (2) 5824
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 204447
97.2%
Connector Punctuation 4938
 
2.3%
Dash Punctuation 886
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 38381
18.8%
3 38347
18.8%
4 33427
16.3%
1 21430
10.5%
5 21312
10.4%
0 11669
 
5.7%
8 10410
 
5.1%
9 10318
 
5.0%
6 10069
 
4.9%
7 9084
 
4.4%
Connector Punctuation
ValueCountFrequency (%)
_ 4938
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 886
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 210271
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 38381
18.3%
3 38347
18.2%
4 33427
15.9%
1 21430
10.2%
5 21312
10.1%
0 11669
 
5.5%
8 10410
 
5.0%
9 10318
 
4.9%
6 10069
 
4.8%
7 9084
 
4.3%
Other values (2) 5824
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210271
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 38381
18.3%
3 38347
18.2%
4 33427
15.9%
1 21430
10.2%
5 21312
10.1%
0 11669
 
5.5%
8 10410
 
5.0%
9 10318
 
4.9%
6 10069
 
4.8%
7 9084
 
4.3%
Other values (2) 5824
 
2.8%

SSN
Text

MISSING 

Distinct12500
Distinct (%)13.2%
Missing5572
Missing (%)5.6%
Memory size7.1 MiB
2023-11-20T14:31:13.436648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters1038609
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row821-00-0265
2nd row821-00-0265
3rd row821-00-0265
4th row821-00-0265
5th row821-00-0265
ValueCountFrequency (%)
078-73-5990 8
 
< 0.1%
457-44-5854 8
 
< 0.1%
063-08-8008 8
 
< 0.1%
902-94-4726 8
 
< 0.1%
596-27-5459 8
 
< 0.1%
782-43-1037 8
 
< 0.1%
799-70-1935 8
 
< 0.1%
329-28-6277 8
 
< 0.1%
718-37-2610 8
 
< 0.1%
201-91-3081 8
 
< 0.1%
Other values (12490) 94339
99.9%
2023-11-20T14:31:13.869324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 188838
18.2%
1 86546
8.3%
8 86026
8.3%
4 85907
8.3%
2 85175
8.2%
7 85109
8.2%
0 84788
8.2%
9 84675
8.2%
5 84648
8.2%
3 83576
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 849771
81.8%
Dash Punctuation 188838
 
18.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 86546
10.2%
8 86026
10.1%
4 85907
10.1%
2 85175
10.0%
7 85109
10.0%
0 84788
10.0%
9 84675
10.0%
5 84648
10.0%
3 83576
9.8%
6 83321
9.8%
Dash Punctuation
ValueCountFrequency (%)
- 188838
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1038609
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 188838
18.2%
1 86546
8.3%
8 86026
8.3%
4 85907
8.3%
2 85175
8.2%
7 85109
8.2%
0 84788
8.2%
9 84675
8.2%
5 84648
8.2%
3 83576
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1038609
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 188838
18.2%
1 86546
8.3%
8 86026
8.3%
4 85907
8.3%
2 85175
8.2%
7 85109
8.2%
0 84788
8.2%
9 84675
8.2%
5 84648
8.2%
3 83576
8.0%

Occupation
Categorical

MISSING 

Distinct15
Distinct (%)< 0.1%
Missing7061
Missing (%)7.1%
Memory size6.8 MiB
Lawyer
6574 
Architect
6355 
Engineer
6349 
Scientist
6299 
Mechanic
6290 
Other values (10)
61063 

Length

Max length13
Median length10
Mean length8.5399333
Min length6

Characters and Unicode

Total characters793616
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowScientist
2nd rowScientist
3rd rowScientist
4th rowScientist
5th rowScientist

Common Values

ValueCountFrequency (%)
Lawyer 6574
 
6.6%
Architect 6355
 
6.4%
Engineer 6349
 
6.3%
Scientist 6299
 
6.3%
Mechanic 6290
 
6.3%
Accountant 6271
 
6.3%
Developer 6234
 
6.2%
Media_Manager 6232
 
6.2%
Teacher 6215
 
6.2%
Entrepreneur 6174
 
6.2%
Other values (5) 29937
29.9%
(Missing) 7061
 
7.1%

Length

2023-11-20T14:31:14.031616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lawyer 6574
 
7.1%
architect 6355
 
6.8%
engineer 6349
 
6.8%
scientist 6299
 
6.8%
mechanic 6290
 
6.8%
accountant 6271
 
6.7%
developer 6234
 
6.7%
media_manager 6232
 
6.7%
teacher 6215
 
6.7%
entrepreneur 6174
 
6.6%
Other values (5) 29937
32.2%

Most occurring characters

ValueCountFrequency (%)
e 112192
14.1%
r 86392
10.9%
n 74376
9.4%
a 67986
 
8.6%
c 62342
 
7.9%
t 62077
 
7.8%
i 61614
 
7.8%
o 30758
 
3.9%
M 30638
 
3.9%
u 24439
 
3.1%
Other values (18) 180802
22.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 688222
86.7%
Uppercase Letter 99162
 
12.5%
Connector Punctuation 6232
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 112192
16.3%
r 86392
12.6%
n 74376
10.8%
a 67986
9.9%
c 62342
9.1%
t 62077
9.0%
i 61614
9.0%
o 30758
 
4.5%
u 24439
 
3.6%
h 18860
 
2.7%
Other values (8) 87186
12.7%
Uppercase Letter
ValueCountFrequency (%)
M 30638
30.9%
A 12626
12.7%
E 12523
12.6%
D 12319
12.4%
L 6574
 
6.6%
S 6299
 
6.4%
T 6215
 
6.3%
J 6083
 
6.1%
W 5885
 
5.9%
Connector Punctuation
ValueCountFrequency (%)
_ 6232
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 787384
99.2%
Common 6232
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 112192
14.2%
r 86392
11.0%
n 74376
9.4%
a 67986
 
8.6%
c 62342
 
7.9%
t 62077
 
7.9%
i 61614
 
7.8%
o 30758
 
3.9%
M 30638
 
3.9%
u 24439
 
3.1%
Other values (17) 174570
22.2%
Common
ValueCountFrequency (%)
_ 6232
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 793616
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 112192
14.1%
r 86392
10.9%
n 74376
9.4%
a 67986
 
8.6%
c 62342
 
7.9%
t 62077
 
7.8%
i 61614
 
7.8%
o 30758
 
3.9%
M 30638
 
3.9%
u 24439
 
3.1%
Other values (18) 180802
22.8%

Annual_Income
Real number (ℝ)

HIGH CORRELATION 

Distinct13487
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean176426.79
Minimum7005.93
Maximum24198062
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-11-20T14:31:14.166560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7005.93
5-th percentile9743.51
Q119456.495
median37578.61
Q372790.92
95-th percentile134533.32
Maximum24198062
Range24191056
Interquartile range (IQR)53334.425

Descriptive statistics

Standard deviation1429681.9
Coefficient of variation (CV)8.1035419
Kurtosis164.37363
Mean176426.79
Median Absolute Deviation (MAD)21450.215
Skewness12.5116
Sum1.7641091 × 1010
Variance2.0439903 × 1012
MonotonicityNot monotonic
2023-11-20T14:31:14.316478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9141.63 16
 
< 0.1%
22434.16 16
 
< 0.1%
17273.83 16
 
< 0.1%
109945.32 16
 
< 0.1%
32543.38 16
 
< 0.1%
17816.75 16
 
< 0.1%
20867.67 16
 
< 0.1%
36585.12 16
 
< 0.1%
40341.16 16
 
< 0.1%
95596.35 15
 
< 0.1%
Other values (13477) 99832
99.8%
ValueCountFrequency (%)
7005.93 8
< 0.1%
7006.035 8
< 0.1%
7006.52 8
< 0.1%
7011.685 8
< 0.1%
7012.31 8
< 0.1%
7019.435 8
< 0.1%
7020.545 8
< 0.1%
7021.91 8
< 0.1%
7023.16 8
< 0.1%
7039.745 8
< 0.1%
ValueCountFrequency (%)
24198062 1
< 0.1%
24188807 1
< 0.1%
24177153 1
< 0.1%
24160009 1
< 0.1%
24105369 1
< 0.1%
24105151 1
< 0.1%
24096975 1
< 0.1%
24065688 1
< 0.1%
24008957 1
< 0.1%
23942655 1
< 0.1%

Monthly_Inhand_Salary
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13235
Distinct (%)15.6%
Missing15001
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean4194.1502
Minimum303.64542
Maximum15204.633
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-11-20T14:31:14.449513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum303.64542
5-th percentile836.12583
Q11625.5583
median3093.745
Q35957.4483
95-th percentile10829.284
Maximum15204.633
Range14900.988
Interquartile range (IQR)4331.89

Descriptive statistics

Standard deviation3183.6987
Coefficient of variation (CV)0.75908076
Kurtosis0.61318177
Mean4194.1502
Median Absolute Deviation (MAD)1754.0842
Skewness1.1272859
Sum3.5646083 × 108
Variance10135938
MonotonicityNot monotonic
2023-11-20T14:31:14.582954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6769.13 15
 
< 0.1%
6082.1875 15
 
< 0.1%
2295.058333 15
 
< 0.1%
6358.956667 15
 
< 0.1%
3080.555 14
 
< 0.1%
6639.56 13
 
< 0.1%
5766.491667 13
 
< 0.1%
4387.2725 13
 
< 0.1%
536.43125 12
 
< 0.1%
1315.560833 11
 
< 0.1%
Other values (13225) 84854
84.9%
(Missing) 15001
 
15.0%
ValueCountFrequency (%)
303.6454167 8
< 0.1%
319.55625 7
< 0.1%
332.1283333 7
< 0.1%
332.43125 6
< 0.1%
333.5966667 6
< 0.1%
355.2083333 8
< 0.1%
357.2558333 7
< 0.1%
358.0583333 6
< 0.1%
361.6033333 6
< 0.1%
368.3741667 7
< 0.1%
ValueCountFrequency (%)
15204.63333 7
< 0.1%
15167.18 8
< 0.1%
15136.69667 7
< 0.1%
15115.19 7
< 0.1%
15101.94 8
< 0.1%
15091.08667 5
< 0.1%
15090.07667 7
< 0.1%
15066.78333 7
< 0.1%
15038.31667 5
< 0.1%
14978.33667 7
< 0.1%

Num_Bank_Accounts
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct943
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.092458
Minimum-1
Maximum1798
Zeros4328
Zeros (%)4.3%
Negative21
Negative (%)< 0.1%
Memory size1.5 MiB
2023-11-20T14:31:14.699097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q13
median6
Q37
95-th percentile10
Maximum1798
Range1799
Interquartile range (IQR)4

Descriptive statistics

Standard deviation117.41005
Coefficient of variation (CV)6.8691143
Kurtosis132.49766
Mean17.092458
Median Absolute Deviation (MAD)2
Skewness11.201968
Sum1709092
Variance13785.12
MonotonicityNot monotonic
2023-11-20T14:31:14.849246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 13001
13.0%
7 12823
12.8%
8 12765
12.8%
4 12184
12.2%
5 12117
12.1%
3 11946
11.9%
9 5443
5.4%
10 5246
5.2%
1 4489
 
4.5%
0 4328
 
4.3%
Other values (933) 5649
5.6%
ValueCountFrequency (%)
-1 21
 
< 0.1%
0 4328
 
4.3%
1 4489
 
4.5%
2 4304
 
4.3%
3 11946
11.9%
4 12184
12.2%
5 12117
12.1%
6 13001
13.0%
7 12823
12.8%
8 12765
12.8%
ValueCountFrequency (%)
1798 2
< 0.1%
1794 2
< 0.1%
1793 1
< 0.1%
1789 2
< 0.1%
1786 1
< 0.1%
1784 2
< 0.1%
1783 1
< 0.1%
1782 1
< 0.1%
1779 2
< 0.1%
1778 1
< 0.1%

Num_Credit_Card
Real number (ℝ)

Distinct1179
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.475983
Minimum0
Maximum1499
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-11-20T14:31:14.982310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q14
median5
Q37
95-th percentile10
Maximum1499
Range1499
Interquartile range (IQR)3

Descriptive statistics

Standard deviation129.06311
Coefficient of variation (CV)5.7422678
Kurtosis74.537319
Mean22.475983
Median Absolute Deviation (MAD)2
Skewness8.4576208
Sum2247396
Variance16657.287
MonotonicityNot monotonic
2023-11-20T14:31:15.115454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 18458
18.5%
7 16614
16.6%
6 16558
16.6%
4 14026
14.0%
3 13276
13.3%
8 4956
 
5.0%
10 4859
 
4.9%
9 4643
 
4.6%
2 2149
 
2.1%
1 2132
 
2.1%
Other values (1169) 2320
 
2.3%
ValueCountFrequency (%)
0 13
 
< 0.1%
1 2132
 
2.1%
2 2149
 
2.1%
3 13276
13.3%
4 14026
14.0%
5 18458
18.5%
6 16558
16.6%
7 16614
16.6%
8 4956
 
5.0%
9 4643
 
4.6%
ValueCountFrequency (%)
1499 2
< 0.1%
1498 3
< 0.1%
1497 3
< 0.1%
1496 2
< 0.1%
1495 1
 
< 0.1%
1494 1
 
< 0.1%
1493 2
< 0.1%
1492 2
< 0.1%
1490 2
< 0.1%
1489 1
 
< 0.1%

Interest_Rate
Real number (ℝ)

HIGH CORRELATION 

Distinct1750
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.471562
Minimum1
Maximum5797
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-11-20T14:31:15.252808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median13
Q320
95-th percentile33
Maximum5797
Range5796
Interquartile range (IQR)12

Descriptive statistics

Standard deviation466.44324
Coefficient of variation (CV)6.4362245
Kurtosis85.17439
Mean72.471562
Median Absolute Deviation (MAD)6
Skewness9.0055958
Sum7246504
Variance217569.3
MonotonicityNot monotonic
2023-11-20T14:31:15.382198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 5011
 
5.0%
5 4979
 
5.0%
6 4719
 
4.7%
10 4540
 
4.5%
12 4538
 
4.5%
7 4494
 
4.5%
9 4493
 
4.5%
11 4428
 
4.4%
18 4102
 
4.1%
15 3992
 
4.0%
Other values (1740) 54695
54.7%
ValueCountFrequency (%)
1 2683
2.7%
2 2465
2.5%
3 2764
2.8%
4 2589
2.6%
5 4979
5.0%
6 4719
4.7%
7 4494
4.5%
8 5011
5.0%
9 4493
4.5%
10 4540
4.5%
ValueCountFrequency (%)
5797 1
< 0.1%
5789 1
< 0.1%
5788 1
< 0.1%
5776 1
< 0.1%
5775 1
< 0.1%
5774 1
< 0.1%
5773 1
< 0.1%
5771 1
< 0.1%
5770 1
< 0.1%
5763 1
< 0.1%

NumofLoan
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct414
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0099409
Minimum-100
Maximum1496
Zeros10930
Zeros (%)10.9%
Negative3876
Negative (%)3.9%
Memory size1.5 MiB
2023-11-20T14:31:15.515404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile0
Q11
median3
Q35
95-th percentile8
Maximum1496
Range1596
Interquartile range (IQR)4

Descriptive statistics

Standard deviation62.650694
Coefficient of variation (CV)20.814593
Kurtosis308.19121
Mean3.0099409
Median Absolute Deviation (MAD)2
Skewness15.979335
Sum300967
Variance3925.1094
MonotonicityNot monotonic
2023-11-20T14:31:15.648438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 15103
15.1%
2 15028
15.0%
4 14743
14.7%
0 10930
10.9%
1 10604
10.6%
6 7803
7.8%
7 7343
7.3%
5 7197
7.2%
-100 3876
 
3.9%
9 3701
 
3.7%
Other values (404) 3663
 
3.7%
ValueCountFrequency (%)
-100 3876
 
3.9%
0 10930
10.9%
1 10604
10.6%
2 15028
15.0%
3 15103
15.1%
4 14743
14.7%
5 7197
7.2%
6 7803
7.8%
7 7343
7.3%
8 3191
 
3.2%
ValueCountFrequency (%)
1496 1
 
< 0.1%
1495 1
 
< 0.1%
1485 1
 
< 0.1%
1484 1
 
< 0.1%
1482 1
 
< 0.1%
1480 3
< 0.1%
1478 1
 
< 0.1%
1474 1
 
< 0.1%
1470 1
 
< 0.1%
1465 1
 
< 0.1%

Type_of_Loan
Text

MISSING 

Distinct6260
Distinct (%)7.1%
Missing11408
Missing (%)11.4%
Memory size11.6 MiB
2023-11-20T14:31:15.781930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length182
Median length142
Mean length66.685064
Min length9

Characters and Unicode

Total characters5907163
Distinct characters33
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAuto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan
2nd rowAuto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan
3rd rowAuto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan
4th rowAuto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan
5th rowAuto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan
ValueCountFrequency (%)
loan 313645
36.4%
and 77457
 
9.0%
payday 40566
 
4.7%
credit-builder 40437
 
4.7%
not 39614
 
4.6%
specified 39614
 
4.6%
home 39098
 
4.5%
equity 39098
 
4.5%
student 38962
 
4.5%
mortgage 38931
 
4.5%
Other values (4) 154426
17.9%
2023-11-20T14:31:16.064576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
773265
13.1%
o 624489
10.6%
a 588827
 
10.0%
n 546501
 
9.3%
e 354755
 
6.0%
t 351543
 
6.0%
d 316248
 
5.4%
L 313645
 
5.3%
i 276750
 
4.7%
, 264676
 
4.5%
Other values (23) 1496464
25.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4003957
67.8%
Uppercase Letter 824828
 
14.0%
Space Separator 773265
 
13.1%
Other Punctuation 264676
 
4.5%
Dash Punctuation 40437
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 624489
15.6%
a 588827
14.7%
n 546501
13.6%
e 354755
8.9%
t 351543
8.8%
d 316248
7.9%
i 276750
6.9%
r 158692
 
4.0%
u 156486
 
3.9%
y 120230
 
3.0%
Other values (9) 509436
12.7%
Uppercase Letter
ValueCountFrequency (%)
L 313645
38.0%
P 79453
 
9.6%
C 79212
 
9.6%
S 78576
 
9.5%
B 40437
 
4.9%
N 39614
 
4.8%
H 39098
 
4.7%
E 39098
 
4.7%
M 38931
 
4.7%
D 38775
 
4.7%
Space Separator
ValueCountFrequency (%)
773265
100.0%
Other Punctuation
ValueCountFrequency (%)
, 264676
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 40437
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4828785
81.7%
Common 1078378
 
18.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 624489
12.9%
a 588827
12.2%
n 546501
11.3%
e 354755
 
7.3%
t 351543
 
7.3%
d 316248
 
6.5%
L 313645
 
6.5%
i 276750
 
5.7%
r 158692
 
3.3%
u 156486
 
3.2%
Other values (20) 1140849
23.6%
Common
ValueCountFrequency (%)
773265
71.7%
, 264676
 
24.5%
- 40437
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5907163
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
773265
13.1%
o 624489
10.6%
a 588827
 
10.0%
n 546501
 
9.3%
e 354755
 
6.0%
t 351543
 
6.0%
d 316248
 
5.4%
L 313645
 
5.3%
i 276750
 
4.7%
, 264676
 
4.5%
Other values (23) 1496464
25.3%

Delay_from_due_date
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct73
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.069406
Minimum-5
Maximum67
Zeros1195
Zeros (%)1.2%
Negative591
Negative (%)0.6%
Memory size1.5 MiB
2023-11-20T14:31:16.198105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-5
5-th percentile3
Q110
median18
Q328
95-th percentile54
Maximum67
Range72
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.860516
Coefficient of variation (CV)0.7053125
Kurtosis0.34803405
Mean21.069406
Median Absolute Deviation (MAD)9
Skewness0.96630415
Sum2106751
Variance220.83493
MonotonicityNot monotonic
2023-11-20T14:31:16.347796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 3595
 
3.6%
13 3424
 
3.4%
8 3324
 
3.3%
14 3312
 
3.3%
10 3278
 
3.3%
7 3234
 
3.2%
9 3233
 
3.2%
11 3182
 
3.2%
12 3141
 
3.1%
6 3137
 
3.1%
Other values (63) 67131
67.1%
ValueCountFrequency (%)
-5 33
 
< 0.1%
-4 62
 
0.1%
-3 118
 
0.1%
-2 168
 
0.2%
-1 210
 
0.2%
0 1195
1.2%
1 1326
1.3%
2 1342
1.3%
3 1686
1.7%
4 1721
1.7%
ValueCountFrequency (%)
67 22
 
< 0.1%
66 32
 
< 0.1%
65 56
 
0.1%
64 64
 
0.1%
63 69
 
0.1%
62 545
0.5%
61 514
0.5%
60 533
0.5%
59 507
0.5%
58 553
0.6%

NumofDelayedPayment
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct711
Distinct (%)0.8%
Missing7002
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean30.9254
Minimum-3
Maximum4397
Zeros1608
Zeros (%)1.6%
Negative644
Negative (%)0.6%
Memory size1.5 MiB
2023-11-20T14:31:16.647738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3
5-th percentile2
Q19
median14
Q318
95-th percentile24
Maximum4397
Range4400
Interquartile range (IQR)9

Descriptive statistics

Standard deviation226.04272
Coefficient of variation (CV)7.3092902
Kurtosis217.16054
Mean30.9254
Median Absolute Deviation (MAD)5
Skewness14.312437
Sum2875722
Variance51095.312
MonotonicityNot monotonic
2023-11-20T14:31:16.780879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 5481
 
5.5%
17 5411
 
5.4%
16 5312
 
5.3%
10 5308
 
5.3%
15 5237
 
5.2%
18 5216
 
5.2%
20 5089
 
5.1%
12 5059
 
5.1%
9 4981
 
5.0%
8 4873
 
4.9%
Other values (701) 41022
41.0%
(Missing) 7002
 
7.0%
ValueCountFrequency (%)
-3 94
 
0.1%
-2 234
 
0.2%
-1 316
 
0.3%
0 1608
1.6%
1 1636
1.6%
2 1810
1.8%
3 1930
1.9%
4 1837
1.8%
5 2090
2.1%
6 2321
2.3%
ValueCountFrequency (%)
4397 1
< 0.1%
4388 1
< 0.1%
4384 1
< 0.1%
4360 1
< 0.1%
4344 1
< 0.1%
4340 1
< 0.1%
4337 1
< 0.1%
4326 1
< 0.1%
4324 1
< 0.1%
4319 1
< 0.1%

ChangedCreditLimit
Real number (ℝ)

MISSING 

Distinct3634
Distinct (%)3.7%
Missing2091
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean10.388885
Minimum-6.49
Maximum36.97
Zeros4
Zeros (%)< 0.1%
Negative1586
Negative (%)1.6%
Memory size1.5 MiB
2023-11-20T14:31:16.913874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-6.49
5-th percentile1.16
Q15.32
median9.4
Q314.87
95-th percentile23.6
Maximum36.97
Range43.46
Interquartile range (IQR)9.55

Descriptive statistics

Standard deviation6.7895288
Coefficient of variation (CV)0.65353778
Kurtosis0.10161209
Mean10.388885
Median Absolute Deviation (MAD)4.57
Skewness0.63885457
Sum1017071.8
Variance46.097701
MonotonicityNot monotonic
2023-11-20T14:31:17.047103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.22 135
 
0.1%
11.5 127
 
0.1%
11.32 126
 
0.1%
7.35 121
 
0.1%
10.06 121
 
0.1%
7.69 116
 
0.1%
8.23 115
 
0.1%
11.49 113
 
0.1%
9.25 110
 
0.1%
7.33 110
 
0.1%
Other values (3624) 96706
96.7%
(Missing) 2091
 
2.1%
ValueCountFrequency (%)
-6.49 1
< 0.1%
-6.48 1
< 0.1%
-6.45 1
< 0.1%
-6.44 2
< 0.1%
-6.43 2
< 0.1%
-6.39 1
< 0.1%
-6.37 1
< 0.1%
-6.35 2
< 0.1%
-6.33 1
< 0.1%
-6.32 2
< 0.1%
ValueCountFrequency (%)
36.97 1
< 0.1%
36.49 1
< 0.1%
36.29 1
< 0.1%
36.09 1
< 0.1%
35.98 1
< 0.1%
35.89 1
< 0.1%
35.84 1
< 0.1%
35.83 1
< 0.1%
35.82 1
< 0.1%
35.69 1
< 0.1%

NumCreditInquiries
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1223
Distinct (%)1.2%
Missing1965
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean27.756187
Minimum0
Maximum2597
Zeros6972
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-11-20T14:31:17.164009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q39
95-th percentile13
Maximum2597
Range2597
Interquartile range (IQR)6

Descriptive statistics

Standard deviation193.1861
Coefficient of variation (CV)6.9601094
Kurtosis100.58758
Mean27.756187
Median Absolute Deviation (MAD)3
Skewness9.7857829
Sum2720828
Variance37320.869
MonotonicityNot monotonic
2023-11-20T14:31:17.299404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 11270
11.3%
3 8888
8.9%
6 8111
8.1%
7 8056
 
8.1%
2 8028
 
8.0%
8 7865
 
7.9%
1 7588
 
7.6%
0 6972
 
7.0%
5 5693
 
5.7%
9 5281
 
5.3%
Other values (1213) 20274
20.3%
ValueCountFrequency (%)
0 6972
7.0%
1 7588
7.6%
2 8028
8.0%
3 8888
8.9%
4 11270
11.3%
5 5693
5.7%
6 8111
8.1%
7 8056
8.1%
8 7865
7.9%
9 5281
5.3%
ValueCountFrequency (%)
2597 1
< 0.1%
2594 1
< 0.1%
2592 2
< 0.1%
2589 2
< 0.1%
2588 1
< 0.1%
2587 1
< 0.1%
2586 1
< 0.1%
2583 1
< 0.1%
2580 1
< 0.1%
2573 1
< 0.1%

CreditMix
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing20193
Missing (%)20.2%
Memory size6.1 MiB
Standard
36476 
Good
24334 
Bad
18988 

Length

Max length8
Median length4
Mean length5.5904659
Min length3

Characters and Unicode

Total characters446108
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGood
2nd rowGood
3rd rowGood
4th rowGood
5th rowGood

Common Values

ValueCountFrequency (%)
Standard 36476
36.5%
Good 24334
24.3%
Bad 18988
19.0%
(Missing) 20193
20.2%

Length

2023-11-20T14:31:17.430161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T14:31:17.530214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
standard 36476
45.7%
good 24334
30.5%
bad 18988
23.8%

Most occurring characters

ValueCountFrequency (%)
d 116274
26.1%
a 91940
20.6%
o 48668
10.9%
S 36476
 
8.2%
t 36476
 
8.2%
n 36476
 
8.2%
r 36476
 
8.2%
G 24334
 
5.5%
B 18988
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 366310
82.1%
Uppercase Letter 79798
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 116274
31.7%
a 91940
25.1%
o 48668
13.3%
t 36476
 
10.0%
n 36476
 
10.0%
r 36476
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
S 36476
45.7%
G 24334
30.5%
B 18988
23.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 446108
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 116274
26.1%
a 91940
20.6%
o 48668
10.9%
S 36476
 
8.2%
t 36476
 
8.2%
n 36476
 
8.2%
r 36476
 
8.2%
G 24334
 
5.5%
B 18988
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 446108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 116274
26.1%
a 91940
20.6%
o 48668
10.9%
S 36476
 
8.2%
t 36476
 
8.2%
n 36476
 
8.2%
r 36476
 
8.2%
G 24334
 
5.5%
B 18988
 
4.3%

OutstandingDebt
Real number (ℝ)

HIGH CORRELATION 

Distinct12203
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1426.2354
Minimum0.23
Maximum4998.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-11-20T14:31:17.646827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.23
5-th percentile118.58
Q1566.05
median1166.23
Q31946.245
95-th percentile4074.215
Maximum4998.07
Range4997.84
Interquartile range (IQR)1380.195

Descriptive statistics

Standard deviation1155.1399
Coefficient of variation (CV)0.80992239
Kurtosis0.90496883
Mean1426.2354
Median Absolute Deviation (MAD)641.7
Skewness1.2075183
Sum1.426107 × 108
Variance1334348.3
MonotonicityNot monotonic
2023-11-20T14:31:17.779921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1360.45 24
 
< 0.1%
460.46 24
 
< 0.1%
1151.7 24
 
< 0.1%
1109.03 24
 
< 0.1%
1448.3 16
 
< 0.1%
82.6 16
 
< 0.1%
1259.92 16
 
< 0.1%
1854.48 16
 
< 0.1%
1266.31 16
 
< 0.1%
1194.38 16
 
< 0.1%
Other values (12193) 99799
99.8%
ValueCountFrequency (%)
0.23 8
< 0.1%
0.34 8
< 0.1%
0.54 8
< 0.1%
0.56 8
< 0.1%
0.77 8
< 0.1%
0.95 16
< 0.1%
1.2 8
< 0.1%
1.23 8
< 0.1%
1.3 8
< 0.1%
1.33 8
< 0.1%
ValueCountFrequency (%)
4998.07 8
< 0.1%
4997.1 8
< 0.1%
4997.05 8
< 0.1%
4992.25 8
< 0.1%
4990.91 8
< 0.1%
4987.19 8
< 0.1%
4986.03 8
< 0.1%
4984.82 8
< 0.1%
4983.86 8
< 0.1%
4982.57 8
< 0.1%

CreditUtilizationRatio
Real number (ℝ)

Distinct99989
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.284965
Minimum20
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-11-20T14:31:17.913359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile24.230737
Q128.052262
median32.305614
Q336.496341
95-th percentile40.220058
Maximum50
Range30
Interquartile range (IQR)8.4440783

Descriptive statistics

Standard deviation5.1168862
Coefficient of variation (CV)0.1584913
Kurtosis-0.94399138
Mean32.284965
Median Absolute Deviation (MAD)4.2215029
Skewness0.02865359
Sum3228206
Variance26.182524
MonotonicityNot monotonic
2023-11-20T14:31:18.048805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.16302264 2
 
< 0.1%
26.40790927 2
 
< 0.1%
27.62620155 1
 
< 0.1%
25.47884058 1
 
< 0.1%
33.93375464 1
 
< 0.1%
30.38103661 1
 
< 0.1%
34.87879316 1
 
< 0.1%
36.52614261 1
 
< 0.1%
33.10942 1
 
< 0.1%
37.08962388 1
 
< 0.1%
Other values (99979) 99979
> 99.9%
ValueCountFrequency (%)
20 1
< 0.1%
20.10076996 1
< 0.1%
20.1729419 1
< 0.1%
20.24413035 1
< 0.1%
20.25707336 1
< 0.1%
20.71974515 1
< 0.1%
20.8309456 1
< 0.1%
20.83248709 1
< 0.1%
20.88008193 1
< 0.1%
20.88125004 1
< 0.1%
ValueCountFrequency (%)
50 1
< 0.1%
49.56451935 1
< 0.1%
49.5223243 1
< 0.1%
49.25498298 1
< 0.1%
49.06427745 1
< 0.1%
48.48985173 1
< 0.1%
48.33729091 1
< 0.1%
48.24700252 1
< 0.1%
48.19982398 1
< 0.1%
48.1917489 1
< 0.1%

Credit_History_Age
Text

MISSING 

Distinct404
Distinct (%)0.4%
Missing9029
Missing (%)9.0%
Memory size7.8 MiB
2023-11-20T14:31:18.245305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length20.986709
Min length20

Characters and Unicode

Total characters1908993
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row22 Years and 1 Months
2nd row22 Years and 3 Months
3rd row22 Years and 4 Months
4th row22 Years and 5 Months
5th row22 Years and 6 Months
ValueCountFrequency (%)
years 90962
20.0%
and 90962
20.0%
months 90962
20.0%
11 10747
 
2.4%
8 10714
 
2.4%
9 10685
 
2.3%
10 10643
 
2.3%
5 10004
 
2.2%
6 9882
 
2.2%
7 9312
 
2.0%
Other values (27) 109937
24.2%
2023-11-20T14:31:18.545827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
363848
19.1%
a 181924
9.5%
s 181924
9.5%
n 181924
9.5%
o 90962
 
4.8%
t 90962
 
4.8%
Y 90962
 
4.8%
e 90962
 
4.8%
r 90962
 
4.8%
d 90962
 
4.8%
Other values (12) 453601
23.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1091544
57.2%
Space Separator 363848
 
19.1%
Decimal Number 271677
 
14.2%
Uppercase Letter 181924
 
9.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 76351
28.1%
2 44996
16.6%
3 25460
 
9.4%
0 24359
 
9.0%
9 18280
 
6.7%
8 18228
 
6.7%
6 17135
 
6.3%
7 16652
 
6.1%
5 16380
 
6.0%
4 13836
 
5.1%
Lowercase Letter
ValueCountFrequency (%)
a 181924
16.7%
s 181924
16.7%
n 181924
16.7%
o 90962
8.3%
t 90962
8.3%
e 90962
8.3%
r 90962
8.3%
d 90962
8.3%
h 90962
8.3%
Uppercase Letter
ValueCountFrequency (%)
Y 90962
50.0%
M 90962
50.0%
Space Separator
ValueCountFrequency (%)
363848
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1273468
66.7%
Common 635525
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
363848
57.3%
1 76351
 
12.0%
2 44996
 
7.1%
3 25460
 
4.0%
0 24359
 
3.8%
9 18280
 
2.9%
8 18228
 
2.9%
6 17135
 
2.7%
7 16652
 
2.6%
5 16380
 
2.6%
Latin
ValueCountFrequency (%)
a 181924
14.3%
s 181924
14.3%
n 181924
14.3%
o 90962
7.1%
t 90962
7.1%
Y 90962
7.1%
e 90962
7.1%
r 90962
7.1%
d 90962
7.1%
M 90962
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1908993
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
363848
19.1%
a 181924
9.5%
s 181924
9.5%
n 181924
9.5%
o 90962
 
4.8%
t 90962
 
4.8%
Y 90962
 
4.8%
e 90962
 
4.8%
r 90962
 
4.8%
d 90962
 
4.8%
Other values (12) 453601
23.8%

Payment_of_Min_Amount
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.4 MiB
Yes
52323 
No
35663 
NM
12005 

Length

Max length3
Median length3
Mean length2.5232771
Min length2

Characters and Unicode

Total characters252305
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
Yes 52323
52.3%
No 35663
35.7%
NM 12005
 
12.0%

Length

2023-11-20T14:31:18.677851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T14:31:18.762502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
yes 52323
52.3%
no 35663
35.7%
nm 12005
 
12.0%

Most occurring characters

ValueCountFrequency (%)
Y 52323
20.7%
e 52323
20.7%
s 52323
20.7%
N 47668
18.9%
o 35663
14.1%
M 12005
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 140309
55.6%
Uppercase Letter 111996
44.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y 52323
46.7%
N 47668
42.6%
M 12005
 
10.7%
Lowercase Letter
ValueCountFrequency (%)
e 52323
37.3%
s 52323
37.3%
o 35663
25.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 252305
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 52323
20.7%
e 52323
20.7%
s 52323
20.7%
N 47668
18.9%
o 35663
14.1%
M 12005
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 252305
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y 52323
20.7%
e 52323
20.7%
s 52323
20.7%
N 47668
18.9%
o 35663
14.1%
M 12005
 
4.8%

TotalEMIpermonth
Real number (ℝ)

ZEROS 

Distinct14949
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1403.0978
Minimum0
Maximum82331
Zeros10613
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-11-20T14:31:18.879084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q130.30666
median69.252263
Q3161.22425
95-th percentile437.01275
Maximum82331
Range82331
Interquartile range (IQR)130.91759

Descriptive statistics

Standard deviation8306.3145
Coefficient of variation (CV)5.9199825
Kurtosis52.218035
Mean1403.0978
Median Absolute Deviation (MAD)49.923291
Skewness7.1024374
Sum1.4029715 × 108
Variance68994860
MonotonicityNot monotonic
2023-11-20T14:31:19.014855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10613
 
10.6%
49.57494921 8
 
< 0.1%
66.78293289 8
 
< 0.1%
112.393902 8
 
< 0.1%
39.17656388 8
 
< 0.1%
22.96083536 8
 
< 0.1%
38.66112651 8
 
< 0.1%
188.2716836 8
 
< 0.1%
41.90237944 8
 
< 0.1%
60.81724743 8
 
< 0.1%
Other values (14939) 89306
89.3%
ValueCountFrequency (%)
0 10613
10.6%
4.462837467 8
 
< 0.1%
4.713183572 8
 
< 0.1%
4.865689677 8
 
< 0.1%
4.916138542 8
 
< 0.1%
5.138484696 8
 
< 0.1%
5.218466359 8
 
< 0.1%
5.24927327 7
 
< 0.1%
5.262291048 8
 
< 0.1%
5.351086151 7
 
< 0.1%
ValueCountFrequency (%)
82331 1
< 0.1%
82256 1
< 0.1%
82236 1
< 0.1%
82204 1
< 0.1%
82193 1
< 0.1%
82178 1
< 0.1%
82163 1
< 0.1%
82122 1
< 0.1%
82115 1
< 0.1%
82103 1
< 0.1%

Amountinvestedmonthly
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct91041
Distinct (%)95.3%
Missing4478
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean637.44579
Minimum0
Maximum10000
Zeros169
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-11-20T14:31:19.145255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31.892809
Q174.532858
median135.92153
Q3265.73173
95-th percentile1149.5436
Maximum10000
Range10000
Interquartile range (IQR)191.19888

Descriptive statistics

Standard deviation2043.4013
Coefficient of variation (CV)3.205608
Kurtosis16.869362
Mean637.44579
Median Absolute Deviation (MAD)75.534776
Skewness4.3222789
Sum60884360
Variance4175489
MonotonicityNot monotonic
2023-11-20T14:31:19.295473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 4305
 
4.3%
0 169
 
0.2%
36.66235139 1
 
< 0.1%
89.73848936 1
 
< 0.1%
59.9372585 1
 
< 0.1%
165.1806595 1
 
< 0.1%
62.0308026 1
 
< 0.1%
215.5770592 1
 
< 0.1%
44.61135853 1
 
< 0.1%
195.5938317 1
 
< 0.1%
Other values (91031) 91031
91.0%
(Missing) 4478
 
4.5%
ValueCountFrequency (%)
0 169
0.2%
10.01019426 1
 
< 0.1%
10.0114248 1
 
< 0.1%
10.03659961 1
 
< 0.1%
10.05376835 1
 
< 0.1%
10.06823459 1
 
< 0.1%
10.07193677 1
 
< 0.1%
10.1075469 1
 
< 0.1%
10.11661404 1
 
< 0.1%
10.1225566 1
 
< 0.1%
ValueCountFrequency (%)
10000 4305
4.3%
1977.326102 1
 
< 0.1%
1961.21885 1
 
< 0.1%
1944.520747 1
 
< 0.1%
1941.237454 1
 
< 0.1%
1903.080048 1
 
< 0.1%
1901.791695 1
 
< 0.1%
1890.855773 1
 
< 0.1%
1887.535211 1
 
< 0.1%
1885.645318 1
 
< 0.1%

Payment_Behaviour
Categorical

MISSING 

Distinct6
Distinct (%)< 0.1%
Missing7600
Missing (%)7.6%
Memory size8.7 MiB
Low_spent_Small_value_payments
25509 
High_spent_Medium_value_payments
17540 
Low_spent_Medium_value_payments
13859 
High_spent_Large_value_payments
13720 
High_spent_Small_value_payments
11339 

Length

Max length32
Median length31
Mean length30.800922
Min length30

Characters and Unicode

Total characters2845728
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh_spent_Small_value_payments
2nd rowLow_spent_Large_value_payments
3rd rowLow_spent_Medium_value_payments
4th rowLow_spent_Small_value_payments
5th rowHigh_spent_Medium_value_payments

Common Values

ValueCountFrequency (%)
Low_spent_Small_value_payments 25509
25.5%
High_spent_Medium_value_payments 17540
17.5%
Low_spent_Medium_value_payments 13859
13.9%
High_spent_Large_value_payments 13720
13.7%
High_spent_Small_value_payments 11339
11.3%
Low_spent_Large_value_payments 10424
10.4%
(Missing) 7600
 
7.6%

Length

2023-11-20T14:31:19.427874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T14:31:19.546461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
low_spent_small_value_payments 25509
27.6%
high_spent_medium_value_payments 17540
19.0%
low_spent_medium_value_payments 13859
15.0%
high_spent_large_value_payments 13720
14.8%
high_spent_small_value_payments 11339
12.3%
low_spent_large_value_payments 10424
11.3%

Most occurring characters

ValueCountFrequency (%)
_ 369564
13.0%
e 332716
11.7%
a 245774
 
8.6%
s 184782
 
6.5%
p 184782
 
6.5%
n 184782
 
6.5%
t 184782
 
6.5%
l 166087
 
5.8%
m 160638
 
5.6%
u 123790
 
4.4%
Other values (13) 708031
24.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2291382
80.5%
Connector Punctuation 369564
 
13.0%
Uppercase Letter 184782
 
6.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 332716
14.5%
a 245774
10.7%
s 184782
8.1%
p 184782
8.1%
n 184782
8.1%
t 184782
8.1%
l 166087
 
7.2%
m 160638
 
7.0%
u 123790
 
5.4%
v 92391
 
4.0%
Other values (8) 430858
18.8%
Uppercase Letter
ValueCountFrequency (%)
L 73936
40.0%
H 42599
23.1%
S 36848
19.9%
M 31399
17.0%
Connector Punctuation
ValueCountFrequency (%)
_ 369564
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2476164
87.0%
Common 369564
 
13.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 332716
13.4%
a 245774
 
9.9%
s 184782
 
7.5%
p 184782
 
7.5%
n 184782
 
7.5%
t 184782
 
7.5%
l 166087
 
6.7%
m 160638
 
6.5%
u 123790
 
5.0%
v 92391
 
3.7%
Other values (12) 615640
24.9%
Common
ValueCountFrequency (%)
_ 369564
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2845728
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 369564
13.0%
e 332716
11.7%
a 245774
 
8.6%
s 184782
 
6.5%
p 184782
 
6.5%
n 184782
 
6.5%
t 184782
 
6.5%
l 166087
 
5.8%
m 160638
 
5.6%
u 123790
 
4.4%
Other values (13) 708031
24.9%

MonthlyBalance
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct98789
Distinct (%)> 99.9%
Missing1200
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean402.55126
Minimum0.007759665
Maximum1602.0405
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-11-20T14:31:19.679759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.007759665
5-th percentile174.74024
Q1270.10663
median336.73122
Q3470.26294
95-th percentile862.59906
Maximum1602.0405
Range1602.0328
Interquartile range (IQR)200.15631

Descriptive statistics

Standard deviation213.9255
Coefficient of variation (CV)0.53142425
Kurtosis2.955223
Mean402.55126
Median Absolute Deviation (MAD)84.552253
Skewness1.5965364
Sum39768441
Variance45764.119
MonotonicityNot monotonic
2023-11-20T14:31:19.828583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
350.0148691 2
 
< 0.1%
695.0571561 2
 
< 0.1%
312.4940887 1
 
< 0.1%
347.41389 1
 
< 0.1%
254.9709216 1
 
< 0.1%
250.0931678 1
 
< 0.1%
289.7550753 1
 
< 0.1%
260.6258317 1
 
< 0.1%
606.8303891 1
 
< 0.1%
111.9905206 1
 
< 0.1%
Other values (98779) 98779
98.8%
(Missing) 1200
 
1.2%
ValueCountFrequency (%)
0.007759665 1
< 0.1%
0.088627865 1
< 0.1%
0.095482496 1
< 0.1%
0.131135651 1
< 0.1%
0.366147079 1
< 0.1%
0.382557979 1
< 0.1%
0.419123611 1
< 0.1%
0.453456491 1
< 0.1%
0.503582353 1
< 0.1%
0.599640126 1
< 0.1%
ValueCountFrequency (%)
1602.040519 1
< 0.1%
1576.288935 1
< 0.1%
1567.208309 1
< 0.1%
1566.613165 1
< 0.1%
1564.134826 1
< 0.1%
1558.421841 1
< 0.1%
1555.201051 1
< 0.1%
1552.946094 1
< 0.1%
1546.31964 1
< 0.1%
1542.274695 1
< 0.1%

Credit_Score
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
Good
70994 
Poor
28997 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters399964
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGood
2nd rowGood
3rd rowGood
4th rowGood
5th rowGood

Common Values

ValueCountFrequency (%)
Good 70994
71.0%
Poor 28997
29.0%

Length

2023-11-20T14:31:19.961453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T14:31:20.044568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
good 70994
71.0%
poor 28997
29.0%

Most occurring characters

ValueCountFrequency (%)
o 199982
50.0%
G 70994
 
17.8%
d 70994
 
17.8%
P 28997
 
7.2%
r 28997
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 299973
75.0%
Uppercase Letter 99991
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 199982
66.7%
d 70994
 
23.7%
r 28997
 
9.7%
Uppercase Letter
ValueCountFrequency (%)
G 70994
71.0%
P 28997
29.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 399964
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 199982
50.0%
G 70994
 
17.8%
d 70994
 
17.8%
P 28997
 
7.2%
r 28997
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 399964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 199982
50.0%
G 70994
 
17.8%
d 70994
 
17.8%
P 28997
 
7.2%
r 28997
 
7.2%

Interactions

2023-11-20T14:31:06.273785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:44.429385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:45.950866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:47.540507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:49.125638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:50.864485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:52.437725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:54.000825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:55.571951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:57.071096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:58.681576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:00.212466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:01.731936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:03.313924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:04.786613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:06.394636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:44.542076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:46.040760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:47.646394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:49.243062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:50.969449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:52.552376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:54.105829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:55.670213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:57.165905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:58.791175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:00.320247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:01.868951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:03.394473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:04.882297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:06.504769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:44.645046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:46.158093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:47.750123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:49.337140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:51.070612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:52.653558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:54.201519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:55.772624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:57.265229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:58.897297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:00.427961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:01.960181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:03.508752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:04.984080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:06.607492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:44.751844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:46.259582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:47.856483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:49.458410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:51.185540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:52.769018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:54.317095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:55.872536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:57.369801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:59.005795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:00.529459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:02.077733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:03.607641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:05.084273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:06.723803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:44.853400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:46.379746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:47.966610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:49.566058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:51.287025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:52.871874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:54.435804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:55.972676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:57.465229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:59.111976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:00.640599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:02.186164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:03.712605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:05.189623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:06.840760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:44.955800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:46.482598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:48.075496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:49.811419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:51.396374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:52.976361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:54.539688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:56.075557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:57.570550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:59.199216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:00.744285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:02.299145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:03.808465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:05.293341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:06.952197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:45.058436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:46.595297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:48.173172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:49.917159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:51.497578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:53.071177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:54.642915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:56.171665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:57.668617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:59.313118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:00.854226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:02.393962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:03.892339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:05.398725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:07.068812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:45.167673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:46.704815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:48.299784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:50.025867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:51.603706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:53.185732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:54.757408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:56.276240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:57.764678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:59.428643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:00.961683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:02.513584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:04.009695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:05.494228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:07.203789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:45.261026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:46.823095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:48.397907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:50.124785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:51.703818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:53.288144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:54.853989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:56.372608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:57.856657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:59.522989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:01.046239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:02.611615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:04.090983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:05.596341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:07.301840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:45.351149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:46.929332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:48.495482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:50.220829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:51.795035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:53.387753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:54.949991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:56.451648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:57.933366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:59.613032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:01.146000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:02.705508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:04.195574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:05.686069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:07.404532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:45.441845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:47.023957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:48.599536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:50.319838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:51.919337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:53.488542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:55.056193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:56.554828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:58.032896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:59.706214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:01.238234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:02.803688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:04.291414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:05.784961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:07.507484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:45.551274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:47.123573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:48.703129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:50.419237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:52.023225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:53.588541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:55.158062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:56.682006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:58.123178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:59.803216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:01.334246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:02.894392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:04.390329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:05.883373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:07.795080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:45.641162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:47.223948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:48.809268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:50.539336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:52.121969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:53.689414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:55.259994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:56.784803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:58.220108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:59.902907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:01.434008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:03.006467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:04.492668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:05.982257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:07.894911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:45.745831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:47.323626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:48.910841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:50.649848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:52.222309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:53.784345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:55.354705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:56.872117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:58.314941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:59.996223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:01.511945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:03.102441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:04.583421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:06.078239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:07.999919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:45.847301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:47.430846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:49.005939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:50.754521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:52.336690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:53.889306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:55.458654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:56.965357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:30:58.397316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:00.103821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:01.627967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:03.201270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:04.678955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-20T14:31:06.172174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-11-20T14:31:20.128167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Annual_IncomeMonthly_Inhand_SalaryNum_Bank_AccountsNum_Credit_CardInterest_RateNumofLoanDelay_from_due_dateNumofDelayedPaymentChangedCreditLimitNumCreditInquiriesOutstandingDebtCreditUtilizationRatioTotalEMIpermonthAmountinvestedmonthlyMonthlyBalanceMonthOccupationCreditMixPayment_of_Min_AmountPayment_BehaviourCredit_Score
Annual_Income1.0000.975-0.261-0.192-0.278-0.225-0.239-0.254-0.154-0.264-0.2710.1370.4510.5740.5840.0000.0000.0000.0030.0000.010
Monthly_Inhand_Salary0.9751.000-0.262-0.195-0.282-0.226-0.242-0.257-0.157-0.267-0.2760.1380.4550.5900.5990.0000.0260.2920.2290.1930.207
Num_Bank_Accounts-0.261-0.2621.0000.3990.5540.4080.5570.5650.2890.4920.486-0.0640.112-0.162-0.3020.0050.0000.0000.0000.0000.008
Num_Credit_Card-0.192-0.1950.3991.0000.4270.3350.4230.3790.1940.3980.438-0.0470.104-0.120-0.2350.0030.0040.0000.0000.0060.002
Interest_Rate-0.278-0.2820.5540.4271.0000.4670.5490.5460.3290.5810.587-0.0680.146-0.176-0.3330.0060.0010.0050.0040.0000.002
NumofLoan-0.225-0.2260.4080.3350.4671.0000.4160.4140.2860.4930.511-0.0850.497-0.142-0.4400.0050.0000.0000.0000.0000.007
Delay_from_due_date-0.239-0.2420.5570.4230.5490.4161.0000.5460.2700.5020.519-0.0600.138-0.150-0.2940.0000.0270.5810.3580.0430.384
NumofDelayedPayment-0.254-0.2570.5650.3790.5460.4140.5461.0000.2830.4800.476-0.0650.126-0.161-0.3040.0040.0030.0030.0000.0070.000
ChangedCreditLimit-0.154-0.1570.2890.1940.3290.2860.2700.2831.0000.3550.316-0.0420.104-0.100-0.1930.0000.0220.4410.3470.0270.075
NumCreditInquiries-0.264-0.2670.4920.3980.5810.4930.5020.4800.3551.0000.578-0.0690.178-0.162-0.3340.0010.0000.0030.0050.0000.005
OutstandingDebt-0.271-0.2760.4860.4380.5870.5110.5190.4760.3160.5781.000-0.0660.169-0.168-0.3480.0000.0310.5890.3790.0550.505
CreditUtilizationRatio0.1370.138-0.064-0.047-0.068-0.085-0.060-0.065-0.042-0.069-0.0661.0000.0080.0190.1930.0000.0020.0910.0750.0860.052
TotalEMIpermonth0.4510.4550.1120.1040.1460.4970.1380.1260.1040.1780.1690.0081.0000.2700.0130.0040.0000.0060.0010.0040.002
Amountinvestedmonthly0.5740.590-0.162-0.120-0.176-0.142-0.150-0.161-0.100-0.162-0.1680.0190.2701.000-0.0200.0000.0000.0680.0560.0710.048
MonthlyBalance0.5840.599-0.302-0.235-0.333-0.440-0.294-0.304-0.193-0.334-0.3480.1930.013-0.0201.0000.0000.0120.2930.2320.2770.196
Month0.0000.0000.0050.0030.0060.0050.0000.0040.0000.0010.0000.0000.0040.0000.0001.0000.0000.0000.0000.0000.008
Occupation0.0000.0260.0000.0040.0010.0000.0270.0030.0220.0000.0310.0020.0000.0000.0120.0001.0000.0320.0170.0040.028
CreditMix0.0000.2920.0000.0000.0050.0000.5810.0030.4410.0030.5890.0910.0060.0680.2930.0000.0321.0000.5440.0860.387
Payment_of_Min_Amount0.0030.2290.0000.0000.0040.0000.3580.0000.3470.0050.3790.0750.0010.0560.2320.0000.0170.5441.0000.0720.266
Payment_Behaviour0.0000.1930.0000.0060.0000.0000.0430.0070.0270.0000.0550.0860.0040.0710.2770.0000.0040.0860.0721.0000.107
Credit_Score0.0100.2070.0080.0020.0020.0070.3840.0000.0750.0050.5050.0520.0020.0480.1960.0080.0280.3870.2660.1071.000

Missing values

2023-11-20T14:31:08.187875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-20T14:31:08.654210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-20T14:31:09.303537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDCustomer_IDMonthNameAgeSSNOccupationAnnual_IncomeMonthly_Inhand_SalaryNum_Bank_AccountsNum_Credit_CardInterest_RateNumofLoanType_of_LoanDelay_from_due_dateNumofDelayedPaymentChangedCreditLimitNumCreditInquiriesCreditMixOutstandingDebtCreditUtilizationRatioCredit_History_AgePayment_of_Min_AmountTotalEMIpermonthAmountinvestedmonthlyPayment_BehaviourMonthlyBalanceCredit_Score
00x1602CUS_0xd40JanuaryAaron Maashoh23821-00-0265Scientist19114.121824.8433333434Auto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan37.011.274.0NaN809.9826.82262022 Years and 1 MonthsNo49.57494980.415295High_spent_Small_value_payments312.494089Good
10x1603CUS_0xd40FebruaryAaron Maashoh23821-00-0265Scientist19114.12NaN3434Auto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan-1NaN11.274.0Good809.9831.944960NaNNo49.574949118.280222Low_spent_Large_value_payments284.629163Good
20x1604CUS_0xd40MarchAaron Maashoh-500821-00-0265Scientist19114.12NaN3434Auto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan37.0NaN4.0Good809.9828.60935222 Years and 3 MonthsNo49.57494981.699521Low_spent_Medium_value_payments331.209863Good
30x1605CUS_0xd40AprilAaron Maashoh23821-00-0265Scientist19114.12NaN3434Auto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan54.06.274.0Good809.9831.37786222 Years and 4 MonthsNo49.574949199.458074Low_spent_Small_value_payments223.451310Good
40x1606CUS_0xd40MayAaron Maashoh23821-00-0265Scientist19114.121824.8433333434Auto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan6NaN11.274.0Good809.9824.79734722 Years and 5 MonthsNo49.57494941.420153High_spent_Medium_value_payments341.489231Good
50x1607CUS_0xd40JuneAaron Maashoh23821-00-0265Scientist19114.12NaN3434Auto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan84.09.274.0Good809.9827.26225922 Years and 6 MonthsNo49.57494962.430172NaN340.479212Good
60x1608CUS_0xd40JulyAaron Maashoh23821-00-0265Scientist19114.121824.8433333434Auto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan38.011.274.0Good809.9822.53759322 Years and 7 MonthsNo49.574949178.344067Low_spent_Small_value_payments244.565317Good
70x1609CUS_0xd40AugustNaN23NaNScientist19114.121824.8433333434Auto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan36.011.274.0Good809.9823.933795NaNNo49.57494924.785217High_spent_Medium_value_payments358.124168Good
80x160eCUS_0x21b1JanuaryRick Rothackerj28004-07-5839NaN34847.843037.9866672461Credit-Builder Loan34.05.422.0Good605.0324.46403126 Years and 7 MonthsNo18.816215104.291825Low_spent_Small_value_payments470.690627Good
90x160fCUS_0x21b1FebruaryRick Rothackerj28004-07-5839Teacher34847.843037.9866672461Credit-Builder Loan71.07.422.0Good605.0338.55084826 Years and 8 MonthsNo18.81621540.391238High_spent_Large_value_payments484.591214Good
IDCustomer_IDMonthNameAgeSSNOccupationAnnual_IncomeMonthly_Inhand_SalaryNum_Bank_AccountsNum_Credit_CardInterest_RateNumofLoanType_of_LoanDelay_from_due_dateNumofDelayedPaymentChangedCreditLimitNumCreditInquiriesCreditMixOutstandingDebtCreditUtilizationRatioCredit_History_AgePayment_of_Min_AmountTotalEMIpermonthAmountinvestedmonthlyPayment_BehaviourMonthlyBalanceCredit_Score
999900x25fe0CUS_0x8600JulySarah McBridec28031-35-0942Architect20002.881929.906667108295Personal Loan, Auto Loan, Mortgage Loan, Student Loan, and Student Loan3326.018.319.0Bad3571.7025.123535NaNYes60.964772173.275503Low_spent_Large_value_payments228.750392Good
999910x25fe1CUS_0x8600AugustSarah McBridec29031-35-0942Architect20002.881929.906667108295Personal Loan, Auto Loan, Mortgage Loan, Student Loan, and Student Loan3325.018.319.0Bad3571.7037.1407846 Years and 3 MonthsYes60.96477234.662906High_spent_Large_value_payments337.362988Good
999920x25fe6CUS_0x942cJanuaryNicks24078-73-5990Mechanic39628.993359.4158334672Auto Loan, and Student Loan23NaN9.503.0NaN502.3832.99133331 Years and 3 MonthsNo35.104023401.196481Low_spent_Small_value_payments189.641080Poor
999930x25fe7CUS_0x942cFebruaryNicks25078-73-5990Mechanic39628.993359.4158334672Auto Loan, and Student Loan23NaN11.503.0Good502.3829.13544731 Years and 4 MonthsNo58638.000000180.733095Low_spent_Medium_value_payments400.104466Good
999940x25fe8CUS_0x942cMarchNicks25078-73-5990Mechanic39628.993359.4158334672Auto Loan, and Student Loan206.09.503.0NaN502.3839.32356931 Years and 5 MonthsNo35.104023140.581403High_spent_Medium_value_payments410.256158Poor
999950x25fe9CUS_0x942cAprilNicks25078-73-5990Mechanic39628.993359.4158334672Auto Loan, and Student Loan237.011.503.0NaN502.3834.66357231 Years and 6 MonthsNo35.10402360.971333High_spent_Large_value_payments479.866228Poor
999960x25feaCUS_0x942cMayNicks25078-73-5990Mechanic39628.993359.4158334672Auto Loan, and Student Loan187.011.503.0NaN502.3840.56563131 Years and 7 MonthsNo35.10402354.185950High_spent_Medium_value_payments496.651610Poor
999970x25febCUS_0x942cJuneNicks25078-73-5990Mechanic39628.993359.4158334657292Auto Loan, and Student Loan276.011.503.0Good502.3841.25552231 Years and 8 MonthsNo35.10402324.028477High_spent_Large_value_payments516.809083Poor
999980x25fecCUS_0x942cJulyNicks25078-73-5990Mechanic39628.993359.4158334672Auto Loan, and Student Loan20NaN11.503.0Good502.3833.63820831 Years and 9 MonthsNo35.104023251.672582Low_spent_Large_value_payments319.164979Good
999990x25fedCUS_0x942cAugustNicks25078-73-5990Mechanic39628.993359.4158334672Auto Loan, and Student Loan186.011.503.0Good502.3834.19246331 Years and 10 MonthsNo35.104023167.163865NaN393.673696Poor